Photometric points : VHS 1256 b#
This demo is intended as a quick start when using photometric data.
We will use several photometric points of VHS 1256 b obtained with different facilities. The filters considered here are:
Paranal
SPHERE
IRDIS_D_H23_2
IRDIS_D_H23_3
IRDIS_D_K12_1
IRDIS_D_K12_2
NACO
Lp
NB405
Mp
JWST
NIRCam
F250M
F300M
F356W
F410M
F444W
MIRI
F1140C
F1550C
Be sure that ForMoSA is in your environment. If you installed ForMoSA from GitHub and added it locally to your $PYTHONPATH, you want Jupyter to be able to read it. To do that, please run these two lines in your terminal before launching Jupyter Notebook:
echo 'export PYTHONPATH="/YOUR/PATH/TO/FORMOSA:$PYTHONPATH"' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
mkdir -p \$CONDA_PREFIX/etc/conda/activate.d
Also, check that the kernel of your Jupyter Notebook corresponds to the kernel of your local environment.
Finally, verify that you input data is at the correct format described in Guidelines
Imports#
[1]:
from ForMoSA.analysis import Analysis
from ForMoSA.core.config import set_filter_path, PlotsConfig, SPECTRAL_PLOT, PHOTOMETRIC_PLOT
from ForMoSA.config.global_config import ConfigLoader, ConfigGenerator, Config_NS
Root path of your analysis#
Please, copy the demo repository into your ForMoSA working environement
[3]:
demo_path = '/Users/spetrus/Desktop/ForMoSA_pkg/ForMoSA/docs/demos/photo/'
formosa_working_path = '/Users/spetrus/Desktop/ForMoSA_pkg/photo/'
import shutil
shutil.copytree(demo_path, formosa_working_path)
# ---- Path where to store photometric filter data ----
# No need to create the folder, ForMoSA will create it for you
# You can avoid this step. In that case the folder will be created at ~/.
set_filter_path(formosa_working_path + '/filters')
Generation of your configuration file#
If this is the first time you are fitting your data, you will need a configuration file. This file can be generated automatically with default parameters using:
[4]:
generator = ConfigGenerator()
generator.save(formosa_working_path, 'vhs1256b/new_config.ini')
INFO Save config to path /Users/spetrus/Desktop/ForMoSA_pkg/photo/
Modification of your configuration file#
You may want to adapt your configuration file to your analysis. You can do it manually or using this routine. You are encouraged to check the config.ini file to see if you need to modify any other parameters.
[5]:
config_r = open(formosa_working_path + 'vhs1256b/new_config.ini', "rt")
conf = config_r.read()
# Change the path of your data
conf = conf.replace("observation_path = unknown", "observation_path = " + formosa_working_path + "vhs1256b/data/")
# Change the location of the grid after its adaptation to your data
conf = conf.replace("adapt_store_path = unknown", "adapt_store_path = " + formosa_working_path + "adapted_grid/")
# Change the path where the results will be stored after the fit
conf = conf.replace("result_path = unknown", "result_path = " + formosa_working_path + "vhs1256b/results/")
# Change the path of the initial grid. In our case it will be the grid BT-Settl. See the tutorial for downloading it.
path_grid = '/Users/spetrus/Desktop/models_grids/BTSETTL_native.nc'
conf = conf.replace("model_path = unknown", "model_path = " + path_grid)
config_r.close()
config_w = open(formosa_working_path + 'vhs1256b/new_config.ini', "wt")
config_w.write(conf)
config_w.close()
With these lines, you can check the properties of the grid you have selected. This will help you define the parameter space explored by your fit. In our case, the BT-Settl grid explores two parameters:
Teff = 1200, 3000 K
log(g) = 2.5, 5.5 dex
[7]:
config_r = open(formosa_working_path + 'vhs1256b/new_config.ini', "rt")
conf = config_r.read()
# Change the wavelength range considered for the fit
conf = conf.replace('wav_fit = "0.9, 5.0"', 'wav_fit = "2.0, 16.0"')
# Change the paramter space explored by the nested sampling
conf = conf.replace('par1 = NA,', 'par1 = uniform, 1200, 2800,')
conf = conf.replace('par2 = NA,', 'par2 = uniform, 2.5, 5.5,')
# Change the number of living points.
conf = conf.replace('npoints = 50', 'npoints = 100')
# Change the nested sampling algorithm (/!\ only nestle seems to work with jupyter. Launche ForMoSA from a terminal to use the other ones)
conf = conf.replace('ns_algo = pymultinest', 'ns_algo = nestle')
config_r.close()
config_w = open(formosa_working_path + 'vhs1256b/new_config.ini', "wt")
config_w.write(conf)
config_w.close()
Loading of the configuration file#
You have to load the configuration file and pass it to all algorithms. The Nested Sampling class will use only the parameters relevant to the algorithm you are using. You should also save this configuration file to ensure it is consistent with your results.
[8]:
cfg = ConfigLoader(formosa_working_path + 'vhs1256b/new_config.ini')
sections = cfg.load()
config_ns = Config_NS(
nestle=cfg.config['config_nestle'],
pymultinest=cfg.config['config_pymultinest'],
ultranest=cfg.config['config_ultranest']
)
generator = ConfigGenerator(sections)
generator.save(cfg.config['config_path'].result_path)
INFO Config file loaded
INFO Save config to path /Users/spetrus/Desktop/ForMoSA_pkg/photo/vhs1256b/results/
Preparation of the analysis#
You need to define you analysis plan by defining two variables.
[9]:
import os
import shutil
# If this is the first time you are adapting your model grid, or if you need to re-adapt it, set adapted = False
# If you have already adapted your model grid, set adapted = True
adapted = False
if adapted == False:
shutil.rmtree(formosa_working_path + "adapted_grid/")
os.mkdir(formosa_working_path + "adapted_grid/")
# If you want to perform the fit, set fitted = False
# If you only want to generate plots, set fitted = True
fitted = False
analysis = Analysis(cfg.config['config_path'], adapted=adapted, fitted=fitted)
INFO Paths checked
INFO ModelGrid generated from /Users/spetrus/Desktop/models_grids/BTSETTL_native.nc
WARNING Wavelength unit not found in grid attributes. Setting to default: um
/Users/spetrus/Desktop/ForMoSA_pkg/ForMoSA/ForMoSA/grid/model_grid.py:162: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
return list(self.grid.dims.values())
<frozen _collections_abc>:880: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
/Users/spetrus/Desktop/ForMoSA_pkg/ForMoSA/ForMoSA/grid/model_grid.py:158: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
return list(self.grid.dims.keys())
INFO Grid dimensions: (1161983, 22, 7) ('wavelength', 'par1', 'par2')
INFO Loading Observation from FITS file: /Users/spetrus/Desktop/ForMoSA_pkg/photo/vhs1256b/data/VHS1256b_photometry.fits
INFO Detected photometric observation with filter ['F1140C' 'F1550C' 'F250M' 'F300M' 'F356W' 'F410M' 'F444W' 'IRDIS_D_H23_2' 'IRDIS_D_H23_3' 'IRDIS_D_K12_1' 'IRDIS_D_K12_2' 'Lp' 'Mp' 'NB405']
INFO Filter data for Paranal/SPHERE.IRDIS_D_H23_2 successfully found
INFO Setting wavelength unit to um for filter Paranal/SPHERE.IRDIS_D_H23_2 to um
INFO Filter data for Paranal/SPHERE.IRDIS_D_H23_3 successfully found
INFO Setting wavelength unit to um for filter Paranal/SPHERE.IRDIS_D_H23_3 to um
INFO Filter data for Paranal/SPHERE.IRDIS_D_K12_1 successfully found
INFO Setting wavelength unit to um for filter Paranal/SPHERE.IRDIS_D_K12_1 to um
INFO Filter data for Paranal/SPHERE.IRDIS_D_K12_2 successfully found
INFO Setting wavelength unit to um for filter Paranal/SPHERE.IRDIS_D_K12_2 to um
INFO Filter data for JWST/NIRCam.F250M successfully found
INFO Setting wavelength unit to um for filter JWST/NIRCam.F250M to um
INFO Filter data for JWST/NIRCam.F300M successfully found
INFO Setting wavelength unit to um for filter JWST/NIRCam.F300M to um
INFO Filter data for JWST/NIRCam.F356W successfully found
INFO Setting wavelength unit to um for filter JWST/NIRCam.F356W to um
INFO Filter data for Paranal/NACO.Lp successfully found
INFO Setting wavelength unit to um for filter Paranal/NACO.Lp to um
INFO Filter data for Paranal/NACO.NB405 successfully found
INFO Setting wavelength unit to um for filter Paranal/NACO.NB405 to um
INFO Filter data for JWST/NIRCam.F410M successfully found
INFO Setting wavelength unit to um for filter JWST/NIRCam.F410M to um
INFO Filter data for JWST/NIRCam.F444W successfully found
INFO Setting wavelength unit to um for filter JWST/NIRCam.F444W to um
INFO Filter data for Paranal/NACO.Mp successfully found
INFO Setting wavelength unit to um for filter Paranal/NACO.Mp to um
INFO Filter data for JWST/MIRI.F1140C successfully found
INFO Setting wavelength unit to um for filter JWST/MIRI.F1140C to um
INFO Filter data for JWST/MIRI.F1550C successfully found
INFO Setting wavelength unit to um for filter JWST/MIRI.F1550C to um
INFO Adding Photometric Observation with name [JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters] to the set of observations
INFO Set of Observations generated
[10]:
print(analysis.observations.observations[0].Filter[0].filter_path)
/Users/spetrus/filters
Launch of the adaptation#
The grid and the observations will be modified to make them comparable. This step should take less than 10 seconds with this setup, data, and model.
[11]:
analysis.adapt(cfg.config['config_adapt'], cfg.config['config_inversion'])
INFO Adapting Observation: [JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters]
INFO Observation [JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters] is photometric. No adaptation
INFO Observations ['[JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters]'] adapted
INFO Saving all the observations ['[JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters]'] to path /Users/spetrus/Desktop/ForMoSA_pkg/photo/vhs1256b/results/Observations
INFO Saving Observation Observation_[JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters].npz
INFO Filter data for Paranal/SPHERE.IRDIS_D_H23_2 successfully found
INFO Filter data for Paranal/SPHERE.IRDIS_D_H23_3 successfully found
INFO Filter data for Paranal/SPHERE.IRDIS_D_K12_1 successfully found
INFO Filter data for Paranal/SPHERE.IRDIS_D_K12_2 successfully found
INFO Filter data for JWST/NIRCam.F250M successfully found
INFO Filter data for JWST/NIRCam.F300M successfully found
INFO Filter data for JWST/NIRCam.F356W successfully found
INFO Filter data for Paranal/NACO.Lp successfully found
INFO Filter data for Paranal/NACO.NB405 successfully found
INFO Filter data for JWST/NIRCam.F410M successfully found
INFO Filter data for JWST/NIRCam.F444W successfully found
INFO Filter data for Paranal/NACO.Mp successfully found
INFO Filter data for JWST/MIRI.F1140C successfully found
INFO Filter data for JWST/MIRI.F1550C successfully found
INFO Grid dimensions: (1161983, 22, 7) ('wavelength', 'par1', 'par2')
INFO Setting wavelength unit of subgrid [JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters] to um>
INFO Setting wavelength unit to um for filter Paranal/SPHERE.IRDIS_D_H23_2 to um
INFO Setting wavelength unit to um for filter Paranal/SPHERE.IRDIS_D_H23_3 to um
INFO Setting wavelength unit to um for filter Paranal/SPHERE.IRDIS_D_K12_1 to um
INFO Setting wavelength unit to um for filter Paranal/SPHERE.IRDIS_D_K12_2 to um
INFO Setting wavelength unit to um for filter JWST/NIRCam.F250M to um
INFO Setting wavelength unit to um for filter JWST/NIRCam.F300M to um
INFO Setting wavelength unit to um for filter JWST/NIRCam.F356W to um
INFO Setting wavelength unit to um for filter Paranal/NACO.Lp to um
INFO Setting wavelength unit to um for filter Paranal/NACO.NB405 to um
INFO Setting wavelength unit to um for filter JWST/NIRCam.F410M to um
INFO Setting wavelength unit to um for filter JWST/NIRCam.F444W to um
INFO Setting wavelength unit to um for filter Paranal/NACO.Mp to um
INFO Setting wavelength unit to um for filter JWST/MIRI.F1140C to um
INFO Setting wavelength unit to um for filter JWST/MIRI.F1550C to um
INFO Generated restricted Grid. Former grid length: 1161983. New grid length: 668374
0%| | 0/154 [00:00<?, ?it/s]
INFO Parallel adaptation
8%|█████████ | 13/154 [00:00<00:08, 17.28it/s]
WARNING Extraction of model failed : Teff=1400.0, log(g)=2.5,
44%|██████████████████████████████████████████████▉ | 67/154 [00:03<00:05, 16.82it/s]
WARNING Extraction of model failed : Teff=2050.0, log(g)=5.5,
53%|████████████████████████████████████████████████████████▊ | 81/154 [00:04<00:04, 16.72it/s]
WARNING Extraction of model failed : Teff=2150.0, log(g)=5.5,
62%|██████████████████████████████████████████████████████████████████▌ | 95/154 [00:05<00:03, 17.57it/s]
WARNING Extraction of model failed : Teff=2250.0, log(g)=5.5,
71%|███████████████████████████████████████████████████████████████████████████▋ | 109/154 [00:06<00:02, 16.54it/s]
WARNING Extraction of model failed : Teff=2350.0, log(g)=5.5,
INFO Adding Photometric Subgrid with name [JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters] to the set of subgrids
INFO Set of subgrids generated: ['BTSETTL_native_[JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters]_Photometric']
INFO Interpolating between holes of the grid BTSETTL_native_[JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters]_Photometric
INFO BTSETTL_native_[JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters]_Photometric
INFO 1/2 - Teff
INFO 2/2 - log(g)
WARNING path /Users/spetrus/Desktop/ForMoSA_pkg/photo/adapted_grid/Subgrids does not exist. Creating it
INFO Saving all the subgrids ['BTSETTL_native_[JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters]_Photometric'] to path /Users/spetrus/Desktop/ForMoSA_pkg/photo/adapted_grid/Subgrids
INFO Saving Grid adapted_BTSETTL_native_[JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters]_Photometric.nc to /Users/spetrus/Desktop/ForMoSA_pkg/photo/adapted_grid/Subgrids
Launch of the inversion#
The fit will be performed using the chosen nested sampling algorithm. This step should take less than 30 seconds with this setup, data, and model.
[12]:
analysis.nested_sampling(cfg.config['config_parameters'], cfg.config['config_adapt'], cfg.config['config_inversion'], config_NS=config_ns)
INFO Detected grid Parameter with name par1 from config
INFO Adding grid Parameter with name par1 to the set of parameters
INFO Detected grid Parameter with name par2 from config
INFO Adding grid Parameter with name par2 to the set of parameters
INFO Set of parameters generated
INFO Restrict subgris and observations to windows ['2.0, 16.0']
INFO Adding Photometric Subgrid with name [JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters] to the set of subgrids
INFO Adding Photometric Observation with name [JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters] to the set of observations
INFO Algorithm for the Nested Sampling: nestle
INFO Creating directory /Users/spetrus/Desktop/ForMoSA_pkg/photo/vhs1256b/results
INFO Summary for the Nested Sampling parameters: name kind prior value fixed scope obs_index 0 par1 GRID uniform [1200.0, 2800.0] False global None 1 par2 GRID uniform [2.5, 5.5] False global None
it= 557 logz=-16.2113280
INFO Time spent: 35.559372901916504
INFO Saving results
Plot of your results#
You are free to modify the plot parameters using the following lines (examples). The outputs are saved in the results folder if you want to create your own plots.
[13]:
SPECTRAL_PLOT.markersize = 0
PHOTOMETRIC_PLOT.linewidth = 3
analysis.plot(analysis.ns.results, plot_native_model=False)
INFO Plotting Corner plot
INFO Plotting posterior chains for each parameter.
INFO Plotting radar plot of the chains
INFO Plotting best fit and residuals
INFO Adding Photometric Observation with name [JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters] to the set of observations
INFO Plotting all the observations ['[JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters]']
INFO Plotting data for observation [JWST+Paranal]_[MIRI+NACO+NIRCam+SPHERE]_[14filters]
INFO Plotting transmission curve of filter Paranal/SPHERE.IRDIS_D_K12_1
INFO Plotting transmission curve of filter Paranal/SPHERE.IRDIS_D_K12_2
INFO Plotting transmission curve of filter JWST/NIRCam.F250M
INFO Plotting transmission curve of filter JWST/NIRCam.F300M
INFO Plotting transmission curve of filter JWST/NIRCam.F356W
INFO Plotting transmission curve of filter Paranal/NACO.Lp
INFO Plotting transmission curve of filter Paranal/NACO.NB405
INFO Plotting transmission curve of filter JWST/NIRCam.F410M
INFO Plotting transmission curve of filter JWST/NIRCam.F444W
INFO Plotting transmission curve of filter Paranal/NACO.Mp
INFO Plotting transmission curve of filter JWST/MIRI.F1140C
INFO Plotting transmission curve of filter JWST/MIRI.F1550C
[ ]: